This study examined attitudes toward artificial intelligence (AI) among pre-service primary school teachers in Mongolia and evaluated the psychometric properties of a Mongolian adaptation of the Students’ Attitudes toward Artificial Intelligence (SATAI) scale. A cross-sectional survey yielded an analytic sample of 672 students at a public teacher education university, randomly divided by academic-year strata into exploratory (n = 334) and confirmatory (n = 338) subsamples. Factor-retention criteria disagreed, and the exploratory solution did not match the component grouping of the source adaptation. A model reproducing that structure fitted poorly and yielded an inadmissible solution. Of seven measurement models estimated from polychoric correlations by diagonally weighted least squares, a bifactor model fitted best (CFI = 0.975; TLI = 0.970), although RMSEA remained 0.086; a four-factor model fitted comparably, but its factor correlations (0.86–0.93) failed the Fornell–Larcker criterion for every pair. Omega hierarchical (0.949) and explained common variance (0.876) indicated a dominant source of common variance, while subscale-specific reliability was limited; internal consistency of the total score was high (α = 0.972). Attitudes were moderately positive (M = 3.59, SD = 0.85), with enjoyment and educational relevance endorsed more strongly than everyday application and self-rated competence. Metric and scalar invariance across academic years was supported by change-in-fit criteria, and fourth-year students scored higher than all earlier cohorts. The findings favor cautious use of the total score rather than subscale scores, and they provide a descriptive basis for designing and then evaluating structured, ethically informed AI preparation in teacher education rather than evidence that such provision is effective.

Numerous facts, studies, and practical observations substantiate the profound influence of students’ attitudes towards learning on the educational process, its outcomes, and the overall quality of education, particularly when they are training to become educators. Moreover, given the inherent individual disparities, students’ learning attitudes exhibit variability [1]. In light of the current societal context and the rapid pace of development, our study aims to investigate the learning attitudes of university students. In our research, we utilized the Likert Scale-Total-Add Scale Method, employing the “learning attitude scale” devised by psychologist Tao De Qing. We assessed the study attitudes of 1020 students from Mongolian universities and various Chinese universities through the aforementioned questionnaire. The findings underwent comprehensive analysis, involving component analysis, exploratory factor analysis, T-Test, and correlation analysis, all executed using SPSS 25 software. The results of the study affirm the significance of emotional, cognitive, and behavioral components in the learning process, underscoring their intimate connection with students’ learning attitudes. While students’ gender does not exhibit any significant correlation with their learning attitudes, factors such as citizenship, nationality (administrative jurisdiction), field of study, and living environment are found to be relevant.